Apache Flink Demo Projects
Self-contained demo using PyFlink with Gensim+spaCy to find topics in the Flink User Mailing List. All you need is Docker! 🐳
Primary Recommender System: online[matching|ranking...](Flask|Vue) - nearline[model serving|real-time service](Flink|tensorflow serving|redis) - offline[feature engine|model training](Spark|Hdfs(Hbase)|tf)
Python Practices 🐙 offers step-by-step Python exercises, real-world examples, and best coding standards from fundamentals to SOLID OOP for practical, scalable learning and growth.
Sahibinden.com Data Engineering Technical Case Study
Real-time crypto analytics platform built with Kafka, Flink, ClickHouse and Iceberg.
A Makeshift data infrastructure setup for datafirstjobs.com.
Modern Flink 2.x + Kafka 4.x + Iceberg lakehouse demos with SQL, PyFlink, and Java.
Engaging, interactive visualizations crafted with Streamlit, seamlessly powered by Apache Flink in batch mode to reveal deep insights from data.
A lightweight stream processing engine built with PyFlink to demonstrate low-latency data transformations, schema parsing, and event-driven pipeline mechanics.
Production-grade CDC pipeline with exactly-once delivery from PostgreSQL to Apache Iceberg via Debezium, Kafka, and Flink.
Real-time network anomaly detection and business impact analysis using PyFlink, Kafka (KRaft), Neo4j, and Avro. Demonstrates stream processing with event-time semantics, graph-based topology enrichment, and sub-second alerting for telecom autonomous networks.
Low-latency real-time fleet telemetry pipeline. Ingests, validates, and statefully aggregates (sliding event-time windows) vehicle diagnostics under 148ms. Features Kafka buffers, PyFlink stream-processing, S3 checkpoints, and Redis active cache serving. Handles 50k events/sec with custom watermark handling, and schema validation gates.
PyFlink data stream processing utilities 🐿
a complete streaming data pipeline that ingests simulated IoT sensor data, processes it and displays in real time through a simple website
PyFlink streaming pipeline: Kafka → 5-minute tumbling event-time windows → per-customer spend aggregation. Handles late-arriving events with watermarks and BoundedOutOfOrderness strategy.
This project showcases a real-time data streaming pipeline using Apache Flink, Apache Spark, and Grafana. It streams data, stores it in Parquet format, and performs aggregations for insights, with seamless visualization via Grafana dashboards.
Portfolio-grade PyFlink streaming fraud detection with stateful features and explainable risk scoring.
Real-time phishing infrastructure detector from Certificate Transparency logs (DE Zoomcamp 2026 capstone)
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